The importance of genetic interactions in disease has been emphasized by genome sequencing projects which have revealed the astounding genetic diversity in a variety of organisms, from yeast to man. We are now faced with the significant challenge of assigning functions to the thousands of uncharacterized genes, and how to use this information to understand how genes interact to determine cell function in health and disease. Despite amazing technical advances, our understanding of genetic interactions relevant to human disease remains rudimentary, and discovering relevant interactions demands an integrated, systematic approach. The proposed work aims to address this challenge by identifying genetic interactions in a model eukaryote, the budding yeast, Saccharomyces cerevisiae. We developed the Synthetic Genetic Array (SGA) method which automates yeast genetics and enables systematic analysis of genetic interactions. 'The emerging principles of genetic networks suggest that systematic identification of genetic interactions offers the potential to organize genomes into hierarchical maps, grouping genes into coherent pathways. This project will complete a reference genetic interaction map for the eukaryotic cell and will explore the conservation of genetic networks over millions of years of evolution. A complete atlas of genetic interactions will reveal how genes act in concert to specify the cellular programs that control development, differentiation, and disease states. AIM 1: Mapping the reference genetic interaction network. The SGA approach will be used to complete the genetic interaction network map for the budding yeast, a fundamental eukaryotic cell that shares thousands of genes and the basic cellular functions of its human counterparts. The complete genetic interaction altas in yeast will guide development of the first mammalian genetic interaction maps. AIM 2: Surveying genetic interactions in fission yeast. Fission yeast is a model organism that is separated from budding yeast by hundreds of millions of years of evolution. Comparison of genetic interactions between these two unicellular organisms will provide a foundation for understanding genetic networks in human cells. AIM 3: Database and Computational Tools for Cross-Species Analysis of Genetic Interactions. We will develop a web-accessible data warehouse for storage and cross-species analysis of genetic interaction networks. A cross-species database will enable analysis of the general rules governing conservation or rewiring of interactions and prediction of conserved interactions. PUBLIC HEALTH RELEVANCE: This project will produce unique datasets and tools that will reveal how genes interact in normal and diseased cells. Genetic interaction reference maps produced by the project will provide valuable insights into gene function, drug target analysis and the link between genotype and phenotype, including the genetic basis of human disease.